Speaker: Jiaying Zhang

Built with Linux container and cgroup technologies, Kubernetes provides an efficient  framework for deploying different kinds of application workloads across multiple machines and compute platforms. Over the past five years, Kubernetes has evolved to support increasingly complex and diverse classes of applications, such as web services, databases, big data, and AI/ML workloads. As people are adopting Kubernetes to run even more diverse enterprise-class, cloud-native, and web-scalable workloads, we are seeing more requirements on improving the Kubernetes resource management model with better isolation, utilization, and performance consistency, yet provides a flexible and extensible framework for people to enable more hardware and workload-specific optimizations on Kubernetes.

In this talk, we will first provide an overview of the current Kubernetes resource model and best practice guidance on managing compute resources and specifying application resource requirements on Kubernetes. We will then discuss some recent and ongoing work on extending the Kubernetes resource model to provide better resource isolation, support more diverse hardware, facilitate fast and flexible application and resource scaling, and promote more consistent application performance across different compute platforms.